Package 'refitME' reference manual

Title:	Measurement Error Modelling using MCEM
Description:	Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the 'RMarkdown' vignette: "'refitME' R-package tutorial".
Authors:	Jakub Stoklosa [aut, cre] , Wenhan Hwang [aut, ctb], David Warton [aut, ctb]
Maintainer:	Jakub Stoklosa <[email protected]>
License:	GPL-2
Version:	1.2.2
Built:	2024-12-22 06:48:49 UTC
Source:	CRAN

An ANOVA function for fitted `MCEMfit_glm` objects

Description

An ANOVA function for fitted MCEMfit_glm objects.

Usage

anova_MCEMfit_glm(object, ..., dispersion = NULL, test = NULL)
anova_MCEMfit_glm(object, ..., dispersion = NULL, test = NULL)

Arguments

`object`	: fitted model objects of class `MCEMfit_glm`.
`...`	: further arguments passed through to `glm`.
`dispersion`	: the dispersion parameter for the fitting family. By default it is obtained from the object(s).
`test`	: a character string, (partially) matching one of "`Chisq`", "`LRT`", "`Rao`", "`F`" or "`Cp`".

Value

anova_MCEMfit_glm produces output identical to anova.glm.

Author(s)

Jakub Stoklosa, Wen-Han Hwang and David I. Warton.

An ANOVA function for fitted `refitME` objects

Description

An ANOVA function for fitted refitME objects.

Usage

## S3 method for class 'refitME'
anova(object, ..., dispersion = NULL, test = NULL)
## S3 method for class 'refitME'
anova(object, ..., dispersion = NULL, test = NULL)

Arguments

`object`	: fitted model objects of class `refitME`.
`...`	: further arguments passed through to `lm` or `glm`.
`dispersion`	: the dispersion parameter for the fitting family. By default it is obtained from the object(s).
`test`	: a character string, (partially) matching one of "`Chisq`", "`LRT`", "`Rao`", "`F`" or "`Cp`". See `stat.anova`.

Value

anova.refitME produces output identical to anova.lm, anova.glm or anova.gam.

Author(s)

Jakub Stoklosa, Wen-Han Hwang and David I. Warton.

The Corymbia eximia presence-only data set

Description

Data set consisting of presence-only records for the plant species Corymbia eximia, site coordinates 5 covariates for each site.

Usage

Corymbiaeximiadata
Corymbiaeximiadata

Format

A data set that contains: 8 columns with 86,316 observations (or sites). The columns are defined as follows:

X: Longitude coordinate.
Y: Latitude coordinate.
FC: Recorded number of fire counts for each site.
MNT: Recorded minimum temperatures for each site.
MXT: Recorded maximum temperature for each site.
Rain: Recorded rainfall for each site.
D.Main: Recorded distance from nearest major road.
Y.obs: Presences for the plant species Corymbia eximia for each site.

Source

See Renner and Warton (2013) for full details on the data and study.

References

Renner, I. W. and Warton, D. I. (2013). Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics, 69, 274–281.

Examples

# Load the data.

data(Corymbiaeximiadata)
# Load the data.

data(Corymbiaeximiadata)

The Framingham heart study data set

Description

Data set consisting of records of male patients with coronary heart disease collected from the Framingham heart study. The Framinghamdata data consists of binary responses and four predictor variables collected on 'n = 1615' patients.

Usage

Framinghamdata
Framinghamdata

Format

A data set that contains: 5 columns with 1,615 observations. The columns are defined as follows:

Y: Response indicator (binary variable) of first evidence of CHD status of patient.
z1: Serum cholesterol level of patient.
z2: Age of patient.
z3: Smoking indicator - whether the patient smokes.
w1: Systolic blood pressure (SBP) of patient - this is the error contaminated variable, calculated from mean scores. The measurement error is 0.00630, see pp. 112 of Carroll et al. (2006).

Source

See Carroll et al. (2006) for full details on the data and study. Also, see https://github.com/JakubStats/refitME for an RMarkdown vignette of an example that uses the data.

References

Carroll, R. J., Ruppert, D., Stefanski, L. A., and Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective. 2nd Ed. London: Chapman & Hall/CRC.

Examples

# Load the data.

data(Framinghamdata)
# Load the data.

data(Framinghamdata)

Extract log-Likelihoods for `MCEMfit_lm` model objects

Description

Extract log-Likelihoods for MCEMfit_lm model objects. This function subtracts the entropy term from the observed likelihood.

Usage

logLik_MCEMfit_lm(object, REML = FALSE, ...)
logLik_MCEMfit_lm(object, REML = FALSE, ...)

Arguments

`object`	: fitted model objects of class `MCEMfit_lm`.
`REML`	: an optional logical value. If `TRUE` the restricted log-likelihood is returned, else, if `FALSE`, the log-likelihood is returned. Defaults to `FALSE`.
`...`	: further arguments passed through to `lm`.

Value

logLik_MCEMfit_lm produces output identical to logLik.

Author(s)

Jakub Stoklosa, Wen-Han Hwang and David I. Warton.

Extract log-Likelihoods for `refitME` model objects

Description

Extract log-Likelihoods for refitME model objects. This function subtracts the entropy term from the observed likelihood.

Usage

## S3 method for class 'refitME'
logLik(object, ...)
## S3 method for class 'refitME'
logLik(object, ...)

Arguments

`object`	: fitted model objects of class `refitME`.
`...`	: further arguments passed through to `lm` or `glm`.

Value

logLik.refitME produces identical output to logLik but for refitME model objects.

Author(s)

Jakub Stoklosa, Wen-Han Hwang and David I. Warton.

Function for fitting `VGAM` capture-recapture (CR) model using the MCEM algorithm

Description

Function for fitting VGAM capture-recapture (CR) model using the MCEM algorithm where covariates have measurement error.

Usage

MCEMfit_CR(mod, sigma.sq.u, B = 50, epsilon = 1e-05, silent = FALSE)
MCEMfit_CR(mod, sigma.sq.u, B = 50, epsilon = 1e-05, silent = FALSE)

Arguments

`mod`	: a `vglm/vgam` object (this is the naive CR model). Make sure the first $p$ input predictor variables in the naive model are the selected error-contaminated variables.
`sigma.sq.u`	: measurement error (ME) variance. A scalar if there is only one error-contaminated predictor variable, otherwise this must be stored as a vector (of ME variances) or a matrix if the ME covariance matrix is known.
`B`	: the number of Monte Carlo replication values (default is set to 50).
`epsilon`	: a set convergence threshold (default is set to 0.00001).
`silent`	: if `TRUE`, the convergence message (which tells the user if the model has converged and reports the number of iterations required) is suppressed (default is set to `FALSE`).

Value

MCEMfit_CR returns model coefficient and population size estimates with standard errors and the effective sample size.

Warning

This function is still under development. Currently the function can only fit the CR model used in the manuscript. IT DOES NOT SUPPORT ALL VGAM families.

Author(s)

Jakub Stoklosa, Wen-Han Hwang and David I. Warton.

Source

See https://github.com/JakubStats/refitME for an RMarkdown vignette with examples.

References

Stoklosa, J., Hwang, W-H., and Warton, D.I. refitME: Measurement Error Modelling using Monte Carlo Expectation Maximization in R.

Examples

# A VGAM example using the Prinia flaviventris capture-recapture data.

library(refitME)
library(VGAM)

data(Priniadata)

tau <- 17   # No. of capture occasions.
w1 <- Priniadata$w1 # Bird wing length predictor.

CR_naiv <- vglm(cbind(cap, noncap) ~ w1,
   VGAM::posbinomial(omit.constant = TRUE, parallel = TRUE ~ w1),
   data = Priniadata, trace = FALSE)

sigma.sq.u <- 0.37 # ME variance.

CR_MCEM <- refitME(CR_naiv, sigma.sq.u)

detach(package:VGAM)

# A VGAM example using the Prinia flaviventris capture-recapture data.

library(refitME)
library(VGAM)

data(Priniadata)

tau <- 17   # No. of capture occasions.
w1 <- Priniadata$w1 # Bird wing length predictor.

CR_naiv <- vglm(cbind(cap, noncap) ~ w1,
   VGAM::posbinomial(omit.constant = TRUE, parallel = TRUE ~ w1),
   data = Priniadata, trace = FALSE)

sigma.sq.u <- 0.37 # ME variance.

CR_MCEM <- refitME(CR_naiv, sigma.sq.u)

detach(package:VGAM)

Function for wrapping the MCEM algorithm on `gam` objects

Description

Function for wrapping the MCEM algorithm on GAMs where predictors are subject to measurement error/error-in-variables.

Usage

MCEMfit_gam(
  mod,
  family,
  sigma.sq.u,
  B = 50,
  epsilon = 1e-05,
  silent = FALSE,
  ...
)
MCEMfit_gam(
  mod,
  family,
  sigma.sq.u,
  B = 50,
  epsilon = 1e-05,
  silent = FALSE,
  ...
)

Arguments

`mod`	: a `gam` object (this is the naive fitted model). Make sure the first $p$ input predictor variables entered in the naive model are the specified error-contaminated variables. These $p$ predictors also need the measurement error variance to be specified in `sigma.sq.u`, see below.
`family`	: a specified family/distribution.
`sigma.sq.u`	: measurement error (ME) variance. A scalar if there is only one error-contaminated predictor variable, otherwise this must be stored as a vector (of ME variances) or a matrix if the ME covariance matrix is known.
`B`	: the number of Monte Carlo replication values (default is set to 50).
`epsilon`	: convergence threshold (default is set to 0.00001).
`silent`	: if `TRUE`, the convergence message (which tells the user if the model has converged and reports the number of iterations required) is suppressed (default is set to `FALSE`).
`...`	: further arguments passed to `gam`.

Value

MCEMfit_gam returns the original naive fitted model object but coefficient estimates and the covariance matrix have been replaced with the final MCEM model fit. Standard errors and the effective sample size (which diagnose how closely the proposal distribution matches the posterior, see equation (2) of Stoklosa, Hwang and Warton) have also been included as outputs.

Author(s)

Jakub Stoklosa, Wen-Han Hwang and David I. Warton.

Source

See https://github.com/JakubStats/refitME for an RMarkdown vignette with examples.

References

Ganguli, B, Staudenmayer, J., and Wand, M. P. (2005). Additive models with predictors subject to measurement error. Australian & New Zealand Journal of Statistics, 47, 193–202.

Wand, M. P. (2018). SemiPar: Semiparametic Regression. R package version 1.0-4.2., URL https://CRAN.R-project.org/package=SemiPar.

Stoklosa, J., Hwang, W-H., and Warton, D.I. refitME: Measurement Error Modelling using Monte Carlo Expectation Maximization in R.

Examples

# A GAM example using the air pollution data set from the SemiPar package.

library(refitME)
library(SemiPar)
library(mgcv)
library(dplyr)

data(milan.mort)

dat.air <- sample_n(milan.mort, 100) # Takes a random sample of size 100.

Y <- dat.air[, 6]  # Mortality counts.

n <- length(Y)

z1 <- (dat.air[, 1])
z2 <- (dat.air[, 4])
z3 <- (dat.air[, 5])
w1 <- log(dat.air[, 9])  # The error-contaminated predictor (total suspended particles).

dat <- data.frame(cbind(Y, w1, z1, z2, z3))

gam_naiv <- gam(Y ~ s(w1), family = "poisson", data = dat)

sigma.sq.u <- 0.0915 # Measurement error variance.

B <- 10  # Consider increasing this if you want a more accurate answer.

gam_MCEM <- refitME(gam_naiv, sigma.sq.u, B)

plot(gam_MCEM, select = 1)

detach(package:mgcv)

# A GAM example using the air pollution data set from the SemiPar package.

library(refitME)
library(SemiPar)
library(mgcv)
library(dplyr)

data(milan.mort)

dat.air <- sample_n(milan.mort, 100) # Takes a random sample of size 100.

Y <- dat.air[, 6]  # Mortality counts.

n <- length(Y)

z1 <- (dat.air[, 1])
z2 <- (dat.air[, 4])
z3 <- (dat.air[, 5])
w1 <- log(dat.air[, 9])  # The error-contaminated predictor (total suspended particles).

dat <- data.frame(cbind(Y, w1, z1, z2, z3))

gam_naiv <- gam(Y ~ s(w1), family = "poisson", data = dat)

sigma.sq.u <- 0.0915 # Measurement error variance.

B <- 10  # Consider increasing this if you want a more accurate answer.

gam_MCEM <- refitME(gam_naiv, sigma.sq.u, B)

plot(gam_MCEM, select = 1)

detach(package:mgcv)

Function for fitting any likelihood-based model using the MCEM algorithm

Description

Function for wrapping the MCEM algorithm on any likelihood-based model where predictors are subject to measurement error/error-in-variables.

Usage

MCEMfit_gen(
  mod,
  family,
  sigma.sq.u,
  B = 50,
  epsilon = 1e-05,
  silent = FALSE,
  theta.est = 1,
  shape.est = 1,
  ...
)
MCEMfit_gen(
  mod,
  family,
  sigma.sq.u,
  B = 50,
  epsilon = 1e-05,
  silent = FALSE,
  theta.est = 1,
  shape.est = 1,
  ...
)

Arguments

`mod`	: a model object (this is the naive fitted model). Make sure the first $p$ input predictor variables entered in the naive model are the specified error-contaminated variables. These $p$ predictors also need the measurement error variance to be specified in `sigma.sq.u`, see below.
`family`	: a specified family/distribution.
`sigma.sq.u`	: measurement error (ME) variance. A scalar if there is only one error-contaminated predictor variable, otherwise this must be stored as a vector (of ME variances) or a matrix if the ME covariance matrix is known.
`B`	: the number of Monte Carlo replication values (default is set to 50).
`epsilon`	: a set convergence threshold (default is set to 0.00001).
`silent`	: if `TRUE`, the convergence message (which tells the user if the model has converged and reports the number of iterations required) is suppressed (default is set to `FALSE`).
`theta.est`	: an initial value for the dispersion parameter (this is required for fitting negative binomial models).
`shape.est`	: an initial value for the shape parameter (this is required for fitting gamma models).
`...`	: further arguments passed through to the function that was used to fit `mod`, that will be used in refitting. These need only be specified if making changes to the arguments as compared to the original call that produced `mod`.

Value

MCEMfit_gen returns the original naive fitted model object but coefficient estimates and residuals have been replaced with the final MCEM model fit. Standard errors are included and returned, if mod is a class of object accepted by the sandwich package (such as glm, gam, survreg and many more).

Author(s)

Jakub Stoklosa, Wen-Han Hwang and David I. Warton.

References

Carroll, R. J., Ruppert, D., Stefanski, L. A., and Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective. 2nd Ed. London: Chapman & Hall/CRC.

Stoklosa, J., Hwang, W-H., and Warton, D.I. refitME: Measurement Error Modelling using Monte Carlo Expectation Maximization in R.

Function for wrapping the MCEM algorithm on `lm` or `glm` objects

Description

Function for wrapping the MCEM algorithm on GLMs where predictors are subject to measurement error/error-in-variables.

Usage

MCEMfit_glm(
  mod,
  family,
  sigma.sq.u,
  B = 50,
  epsilon = 1e-05,
  silent = FALSE,
  ...
)
MCEMfit_glm(
  mod,
  family,
  sigma.sq.u,
  B = 50,
  epsilon = 1e-05,
  silent = FALSE,
  ...
)

Arguments

`mod`	: a `lm/glm` object (this is the naive fitted model). Make sure the first $p$ input predictor variables entered in the naive model are the specified error-contaminated variables. These $p$ predictors also need the measurement error variance to be specified in `sigma.sq.u`, see below.
`family`	: a specified family/distribution.
`sigma.sq.u`	: measurement error (ME) variance. A scalar if there is only one error-contaminated predictor variable, otherwise this must be stored as a vector (of ME variances) or a matrix if the ME covariance matrix is known.
`B`	: the number of Monte Carlo replication values (default is set to 50).
`epsilon`	: a set convergence threshold (default is set to 0.00001).
`silent`	: if `TRUE`, the convergence message (which tells the user if the model has converged and reports the number of iterations required) is suppressed (default is set to `FALSE`).
`...`	: further arguments passed to `lm` or `glm`.

Value

MCEMfit_glm returns the naive fitted model object where coefficient estimates, the covariance matrix, fitted values, the log-likelihood, and residuals have been replaced with the final MCEM model fit. Standard errors and the effective sample size (which diagnose how closely the proposal distribution matches the posterior, see equation (2) of Stoklosa, Hwang and Warton) have also been included as outputs.

Author(s)

Jakub Stoklosa, Wen-Han Hwang and David I. Warton.

Source

See https://github.com/JakubStats/refitME for an RMarkdown vignette with examples.

References

Carroll, R. J., Ruppert, D., Stefanski, L. A., and Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective. 2nd Ed. London: Chapman & Hall/CRC.

Stoklosa, J., Hwang, W-H., and Warton, D.I. refitME: Measurement Error Modelling using Monte Carlo Expectation Maximization in R.

Examples

# A GLM example I - binary response data.

library(refitME)

data(Framinghamdata)

glm_naiv <- glm(Y ~ w1 + z1 + z2 + z3, x = TRUE, family = binomial, data = Framinghamdata)

# The error-contaminated predictor in this example is systolic blood pressure (w1).

sigma.sq.u <- 0.006295  # ME variance, as obtained from Carroll et al. (2006) monograph.

B <- 50 # The number of Monte Carlo replication values.

glm_MCEM <- refitME(glm_naiv, sigma.sq.u, B)

# A GLM example I - binary response data.

library(refitME)

data(Framinghamdata)

glm_naiv <- glm(Y ~ w1 + z1 + z2 + z3, x = TRUE, family = binomial, data = Framinghamdata)

# The error-contaminated predictor in this example is systolic blood pressure (w1).

sigma.sq.u <- 0.006295  # ME variance, as obtained from Carroll et al. (2006) monograph.

B <- 50 # The number of Monte Carlo replication values.

glm_MCEM <- refitME(glm_naiv, sigma.sq.u, B)

The yellow-bellied Prinia Prinia flaviventris capture-recapture data

Description

Data set consisting of capture-recapture histories 164 uniquely captured birds across 17 weekly capture occasions. Bird wing lengths were also measured in the study.

Usage

Priniadata
Priniadata

Format

A data set that contains: 3 columns with 164 observations. The columns are defined as follows:

w1: Bird wing lengths.
cap: Number of times the individual was captured.
noncap: Number of times the individual was not captured.

Source

See Hwang, Huang and Wang (2007) for full details on the data and study.

References

Hwang, W. H., Huang, S. Y. H., and Wang, C. (2007). Effects of measurement error and conditional score estimation in capture–recapture models. Statistica Sinica, 17, 301-316.

Examples

# Load the data.

data(Priniadata)
# Load the data.

data(Priniadata)

A wrapper function for correcting measurement error in predictor variables via the MCEM algorithm

Description

Function that extracts the fitted (naive) model object and wraps the MCEM algorithm to correct for measurement error/error-in-variables in predictors.

Usage

refitME(mod, sigma.sq.u, B = 50, epsilon = 1e-05, silent = FALSE, ...)
refitME(mod, sigma.sq.u, B = 50, epsilon = 1e-05, silent = FALSE, ...)

Arguments

`mod`	: any (S3 class) fitted object that responds to the generic functions `family`, `model.frame`, `update` and `predict`, and accepts weighted observations via `weights`. The `mod` argument specifies the naive fitted model. Make sure the first $p$ input predictor variables in the naive model are the selected error-contaminated predictors variables. Also, the `mod` argument allows `vlgm/vgam` (S4 class) model objects when using the `posbinomial` family – this is a specific function developed for fitting closed population capture–recapture models, see `MCEMfit_CR`.
`sigma.sq.u`	: measurement error (ME) variance. A scalar if there is only one error-contaminated predictor variable, otherwise this must be stored as a vector (of known ME variances) or a matrix if the ME covariance matrix is known.
`B`	: the number of Monte Carlo replication values (default is set 50).
`epsilon`	: convergence threshold (default is set to 0.00001).
`silent`	: if `TRUE`, the convergence message (which tells the user if the model has converged and reports the number of iterations required) is suppressed (default is set to `FALSE`).
`...`	: further arguments passed through to the function that was used to fit `mod`, that will be used in refitting. These need only be specified if making changes to the arguments as compared to the original call that produced `mod`.

Value

refitME returns the naive fitted model object where coefficient estimates, the covariance matrix, fitted values, the log-likelihood, and residuals have been replaced with the final MCEM model fit. Standard errors are included and returned, if mod is a class of object accepted by the sandwich package (such as glm, gam, survreg and many more). The effective sample size (which diagnose how closely the proposal distribution matches the posterior, see equation (2) of Stoklosa, Hwang and Warton) have also been included as outputs.

Author(s)

Jakub Stoklosa, Wen-Han Hwang and David I. Warton.

Source

See https://github.com/JakubStats/refitME for an RMarkdown vignette with examples.

References

Carroll, R. J., Ruppert, D., Stefanski, L. A., and Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective. 2nd Ed. London: Chapman & Hall/CRC.

Stoklosa, J., Hwang, W-H., and Warton, D.I. refitME: Measurement Error Modelling using Monte Carlo Expectation Maximization in R.

Examples

# A GLM example I - binary response data.

library(refitME)

data(Framinghamdata)

glm_naiv <- glm(Y ~ w1 + z1 + z2 + z3, x = TRUE, family = binomial, data = Framinghamdata)

# The error-contaminated predictor variable in this example is systolic blood pressure (w1).

sigma.sq.u <- 0.01259/2 # ME variance, as obtained from Carroll et al. (2006) monograph.

B <- 50  # The number of Monte Carlo replication values.

glm_MCEM <- refitME(glm_naiv, sigma.sq.u, B)

# A GLM example I - binary response data.

library(refitME)

data(Framinghamdata)

glm_naiv <- glm(Y ~ w1 + z1 + z2 + z3, x = TRUE, family = binomial, data = Framinghamdata)

# The error-contaminated predictor variable in this example is systolic blood pressure (w1).

sigma.sq.u <- 0.01259/2 # ME variance, as obtained from Carroll et al. (2006) monograph.

B <- 50  # The number of Monte Carlo replication values.

glm_MCEM <- refitME(glm_naiv, sigma.sq.u, B)

Function that replaces NA with zero for a matrix

Description

This function replaces NA with zero for a matrix.

Usage

## S3 method for class 'na'
sqrt(x)
## S3 method for class 'na'
sqrt(x)

Arguments

`x`	: a matrix

Value

sqrt.na returns a matrix.

Author(s)

Jakub Stoklosa

Function that calculates a weighted variance

Description

This function that calculates a weighted variance for a given vector.

Usage

wt.var(x, w)
wt.var(x, w)

Arguments

`x`	: a vector of numerical data.
`w`	: a vector of equal length to `x` representing the weights.

Value

wt.var returns a single value from analysis requested.

Source

The developer of this function is Jeremy VanDerWal. See https://rdrr.io/cran/SDMTools/src/R/wt.mean.R

Examples

# Define simple data
x = 1:25 # Set of numbers.
wt = runif(25) # Some arbitrary weights.

# Display variances (unweighted and then weighted).
var(x)
wt.var(x, wt)
# Define simple data
x = 1:25 # Set of numbers.
wt = runif(25) # Some arbitrary weights.

# Display variances (unweighted and then weighted).
var(x)
wt.var(x, wt)

Package 'refitME'

Help Index

An ANOVA function for fitted MCEMfit_glm objects

Description

Usage

Arguments

Value

Author(s)

See Also

An ANOVA function for fitted refitME objects

Description

Usage

Arguments

Value

Author(s)

See Also

The Corymbia eximia presence-only data set

Description

Usage

Format

Source

References

Examples

The Framingham heart study data set

Description

Usage

Format

Source

References

Examples

Extract log-Likelihoods for MCEMfit_lm model objects

Description

Usage

Arguments

Value

Author(s)

See Also

Extract log-Likelihoods for refitME model objects

Description

Usage

Arguments

Value

Author(s)

See Also

Function for fitting VGAM capture-recapture (CR) model using the MCEM algorithm

Description

Usage

Arguments

Value

Warning

Author(s)

Source

References

See Also

Examples

Function for wrapping the MCEM algorithm on gam objects

Description

Usage

Arguments

Value

Author(s)

Source

References

See Also

Examples

Function for fitting any likelihood-based model using the MCEM algorithm

Description

Usage

Arguments

Value

Author(s)

References

See Also

Function for wrapping the MCEM algorithm on lm or glm objects

Description

Usage

Arguments

Value

Author(s)

Source

An ANOVA function for fitted `MCEMfit_glm` objects

An ANOVA function for fitted `refitME` objects

Extract log-Likelihoods for `MCEMfit_lm` model objects

Extract log-Likelihoods for `refitME` model objects

Function for fitting `VGAM` capture-recapture (CR) model using the MCEM algorithm

Function for wrapping the MCEM algorithm on `gam` objects

Function for wrapping the MCEM algorithm on `lm` or `glm` objects